“…That is the gift that David F einberg has brought to U CLA. I am in awe of his management skills."…”

Tabla de Contenidos: “…-- Evaluating on test (holdout) data -- Understanding evaluation metrics -- Evaluating regression models -- R-squared -- Root mean squared error -- Mean absolute error -- When and how to use each metric -- Practical evaluation strategies -- Summarizing the evaluation of regression models -- Evaluating classification models -- Classification model evaluation metrics -- Precision, recall, and F1-Score -- Recall -- F1-score -- Methods for explaining machine learning models -- Making sense of regression models - the power of coefficients -- Decoding classification models - unveiling feature importance -- Beyond specific models - universal insights using SHAP values -- Summary -- Common Pitfalls in Machine Learning -- Understanding the complexity -- Dirty data, damaged models - how data quantity and quality impact ML -- The importance of adequate training data -- Dealing with poor data quality -- Conclusion -- Overcoming overfitting and underfitting -- Navigating training-serving skew and model drift -- Ensuring fairness -- Mastering overfitting and underfitting for optimal model performance…”